zhujiesheng
commented
5 years ago I also have this problem.Have you solved it?
Open ankamv opened 5 years ago
zhujiesheng
commented
5 years ago I also have this problem.Have you solved it?
ankamv
commented
5 years ago Nope. I'm not sure what versions are compatible.
On Sat, May 25, 2019, 10:06 AM zhujiesheng notifications@github.com wrote:
I also have this problem.Have you solved it?
— You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub https://github.com/databricks/spark-deep-learning/issues/194?email_source=notifications&email_token=ABXSMNEJU2ISY7MUCU66XALPXFBVTA5CNFSM4HL3YRJ2YY3PNVWWK3TUL52HS4DFVREXG43VMVBW63LNMVXHJKTDN5WW2ZLOORPWSZGODWHSPGA#issuecomment-495921048, or mute the thread https://github.com/notifications/unsubscribe-auth/ABXSMNDPKUCILCJ6Y5EKTKTPXFBVTANCNFSM4HL3YRJQ .
javadba
commented
5 years ago The docs say spark 2.3.0 . This repo seems to be somewhat poorly maintained so I'd suggest starting with that one .. and then seeing if you can make a patch/PR that provides updated support at spark 2.4.2 level
razorkoo
commented
5 years ago I guess issue is in Scala version, I'm not sure that Scala version you are using but I had the same issue with Scala 2.12. I switched my Scala in 2.11.12 version and it works now
Abhishek-P
commented
4 years ago I am seeing the issue although not in Databrick based pyspark. Rather in normal py spark-nlp mode, but on windows. I have spark-nlp 2.5.5 and Spark 2.4.6.
`I am trying out the ContenxtAwareSpellChecker provided in https://medium.com/spark-nlp/applying-context-aware-spell-checking-in-spark-nlp-3c29c46963bc
The first of the component in the pipeline is a DocumentAssembler
from sparknlp.annotator import *
from sparknlp.base import *
import sparknlp
spark = sparknlp.start()
documentAssembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")The above code when run fails as below
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Users\pab\AppData\Local\Continuum\anaconda3.7\envs\MailChecker\lib\site-packages\pyspark\__init__.py", line 110, in wrapper
return func(self, **kwargs)
File "C:\Users\pab\AppData\Local\Continuum\anaconda3.7\envs\MailChecker\lib\site-packages\sparknlp\base.py", line 148, in __init__
super(DocumentAssembler, self).__init__(classname="com.johnsnowlabs.nlp.DocumentAssembler")
File "C:\Users\pab\AppData\Local\Continuum\anaconda3.7\envs\MailChecker\lib\site-packages\pyspark\__init__.py", line 110, in wrapper
return func(self, **kwargs)
File "C:\Users\pab\AppData\Local\Continuum\anaconda3.7\envs\MailChecker\lib\site-packages\sparknlp\internal.py", line 72, in __init__
self._java_obj = self._new_java_obj(classname, self.uid)
File "C:\Users\pab\AppData\Local\Continuum\anaconda3.7\envs\MailChecker\lib\site-packages\pyspark\ml\wrapper.py", line 69, in _new_java_obj
return java_obj(*java_args)
File "C:\Users\pab\AppData\Local\Continuum\anaconda3.7\envs\MailChecker\lib\site-packages\pyspark\python\lib\py4j-0.10.9-src.zip\py4j\java_gateway.py", line 1569, in __call__
File "C:\Users\pab\AppData\Local\Continuum\anaconda3.7\envs\MailChecker\lib\site-packages\pyspark\sql\utils.py", line 131, in deco
return f(*a, **kw)
File "C:\Users\pab\AppData\Local\Continuum\anaconda3.7\envs\MailChecker\lib\site-packages\pyspark\python\lib\py4j-0.10.9-src.zip\py4j\protocol.py", line 328, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling None.com.johnsnowlabs.nlp.DocumentAssembler.
: java.lang.NoClassDefFoundError: org/apache/spark/ml/util/MLWritable$class
at com.johnsnowlabs.nlp.DocumentAssembler.<init>(DocumentAssembler.scala:16)
at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)
at sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)
at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)
at java.lang.reflect.Constructor.newInstance(Constructor.java:423)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:247)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
at py4j.Gateway.invoke(Gateway.java:238)
at py4j.commands.ConstructorCommand.invokeConstructor(ConstructorCommand.java:80)
at py4j.commands.ConstructorCommand.execute(ConstructorCommand.java:69)
at py4j.GatewayConnection.run(GatewayConnection.java:238)
at java.lang.Thread.run(Thread.java:748)`
aishwarya-agrawal
commented
3 years ago I am getting a similar issue on ubuntu 16.04 with pyspark
import pyspark
spark = pyspark.sql.SparkSession.builder.appName("MyApp") \
.config("spark.jars.packages", "com.microsoft.ml.spark:mmlspark_2.11:1.0.0-rc2") \
.config("spark.jars.repositories", "https://mmlspark.azureedge.net/maven") \
.getOrCreate()
from mmlspark.lightgbm import LightGBMClassifier
LightGBMClassifier()Gives me following error
---------------------------------------------------------------------------
Py4JJavaError Traceback (most recent call last)
<ipython-input-5-f9c170eb7541> in <module>
----> 1 LightGBMClassifier()
~/anaconda3/envs/cl_susp_env/lib/python3.6/site-packages/pyspark/__init__.py in wrapper(self, *args, **kwargs)
108 raise TypeError("Method %s forces keyword arguments." % func.__name__)
109 self._input_kwargs = kwargs
--> 110 return func(self, **kwargs)
111 return wrapper
112
/tmp/spark-3fd59146-3144-47ee-95cc-54bae1c707a4/userFiles-a0a10de8-4ea8-4972-81cf-32431d7f12e9/com.microsoft.ml.spark_mmlspark_2.11-1.0.0-rc2.jar/mmlspark/lightgbm/_LightGBMClassifier.py in __init__(self, baggingFraction, baggingFreq, baggingSeed, binSampleCount, boostFromAverage, boostingType, categoricalSlotIndexes, categoricalSlotNames, defaultListenPort, driverListenPort, earlyStoppingRound, featureFraction, featuresCol, featuresShapCol, improvementTolerance, initScoreCol, isProvideTrainingMetric, isUnbalance, labelCol, lambdaL1, lambdaL2, leafPredictionCol, learningRate, maxBin, maxBinByFeature, maxDeltaStep, maxDepth, metric, minDataInLeaf, minGainToSplit, minSumHessianInLeaf, modelString, negBaggingFraction, numBatches, numIterations, numLeaves, numTasks, objective, parallelism, posBaggingFraction, predictionCol, probabilityCol, rawPredictionCol, repartitionByGroupingColumn, slotNames, thresholds, timeout, topK, useBarrierExecutionMode, validationIndicatorCol, verbosity, weightCol)
81 def __init__(self, baggingFraction=1.0, baggingFreq=0, baggingSeed=3, binSampleCount=200000, boostFromAverage=True, boostingType="gbdt", categoricalSlotIndexes=[], categoricalSlotNames=[], defaultListenPort=12400, driverListenPort=0, earlyStoppingRound=0, featureFraction=1.0, featuresCol="features", featuresShapCol="", improvementTolerance=0.0, initScoreCol=None, isProvideTrainingMetric=False, isUnbalance=False, labelCol="label", lambdaL1=0.0, lambdaL2=0.0, leafPredictionCol="", learningRate=0.1, maxBin=255, maxBinByFeature=[], maxDeltaStep=0.0, maxDepth=-1, metric="", minDataInLeaf=20, minGainToSplit=0.0, minSumHessianInLeaf=0.001, modelString="", negBaggingFraction=1.0, numBatches=0, numIterations=100, numLeaves=31, numTasks=0, objective="binary", parallelism="data_parallel", posBaggingFraction=1.0, predictionCol="prediction", probabilityCol="probability", rawPredictionCol="rawPrediction", repartitionByGroupingColumn=True, slotNames=[], thresholds=None, timeout=1200.0, topK=20, useBarrierExecutionMode=False, validationIndicatorCol=None, verbosity=1, weightCol=None):
82 super(_LightGBMClassifier, self).__init__()
---> 83 self._java_obj = self._new_java_obj("com.microsoft.ml.spark.lightgbm.LightGBMClassifier")
84 self.baggingFraction = Param(self, "baggingFraction", "baggingFraction: Bagging fraction (default: 1.0)")
85 self._setDefault(baggingFraction=1.0)
~/anaconda3/envs/cl_susp_env/lib/python3.6/site-packages/pyspark/ml/wrapper.py in _new_java_obj(java_class, *args)
67 java_obj = getattr(java_obj, name)
68 java_args = [_py2java(sc, arg) for arg in args]
---> 69 return java_obj(*java_args)
70
71 @staticmethod
~/anaconda3/envs/cl_susp_env/lib/python3.6/site-packages/py4j/java_gateway.py in __call__(self, *args)
1567 answer = self._gateway_client.send_command(command)
1568 return_value = get_return_value(
-> 1569 answer, self._gateway_client, None, self._fqn)
1570
1571 for temp_arg in temp_args:
~/anaconda3/envs/cl_susp_env/lib/python3.6/site-packages/pyspark/sql/utils.py in deco(*a, **kw)
129 def deco(*a, **kw):
130 try:
--> 131 return f(*a, **kw)
132 except py4j.protocol.Py4JJavaError as e:
133 converted = convert_exception(e.java_exception)
~/anaconda3/envs/cl_susp_env/lib/python3.6/site-packages/py4j/protocol.py in get_return_value(answer, gateway_client, target_id, name)
326 raise Py4JJavaError(
327 "An error occurred while calling {0}{1}{2}.\n".
--> 328 format(target_id, ".", name), value)
329 else:
330 raise Py4JError(
Py4JJavaError: An error occurred while calling None.com.microsoft.ml.spark.lightgbm.LightGBMClassifier.
: java.lang.NoClassDefFoundError: org/apache/spark/ml/util/MLWritable$class
at com.microsoft.ml.spark.lightgbm.LightGBMClassifier.<init>(LightGBMClassifier.scala:27)
at com.microsoft.ml.spark.lightgbm.LightGBMClassifier.<init>(LightGBMClassifier.scala:30)
at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)
at sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)
at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)
at java.lang.reflect.Constructor.newInstance(Constructor.java:423)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:247)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
at py4j.Gateway.invoke(Gateway.java:238)
at py4j.commands.ConstructorCommand.invokeConstructor(ConstructorCommand.java:80)
at py4j.commands.ConstructorCommand.execute(ConstructorCommand.java:69)
at py4j.GatewayConnection.run(GatewayConnection.java:238)
at java.lang.Thread.run(Thread.java:748)
Caused by: java.lang.ClassNotFoundException: org.apache.spark.ml.util.MLWritable$class
at java.net.URLClassLoader.findClass(URLClassLoader.java:382)
at java.lang.ClassLoader.loadClass(ClassLoader.java:418)
at sun.misc.Launcher$AppClassLoader.loadClass(Launcher.java:352)
at java.lang.ClassLoader.loadClass(ClassLoader.java:351)
... 13 moreEnvironment: ubuntu 16.04 python 3.6 pyspark 3.0.0
khaarthikm
commented
3 years ago Issue is still open. Unfortunately SPARK 3.0 is not compatible with mmlspark
VPisanoCM
commented
2 years ago I think this is still open. Databricks doesn't offer any SPARK versions less than 2.4.3
madhuyadu
commented
2 years ago mmlspark not compatible with Spark 3.0?
I'm using Spark 2.4.2 with Anaconda python 3.6.5. I'm getting below error. What's the best way to resolve this?
Command: pyspark --master local[*] --packages databricks:spark-deep-learning:1.5.0-spark2.4-s_2.11